Due to the lack of structured knowledge applied in learning distributed representation of categories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to compute meaningful seman...

Incorporating multiple types of relational information from heterogeneous networks has been proved effective in data mining. Although Wikipedia is one of the most famous heterogeneous network, previous works of semantic analysis on Wikipedia are mostly limited on single type of relations. In this paper, we aim at incorporating multiple types of relations to measure the semantic relatedness betw...

In this paper, we describe our system submitted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of similarity between two sentences. First approach combines corpus-based semantic relatedness measure over the whole sentence with the knowledge-based semantic similarity scores obtained for the words falling under the same syntac...

A correct semantic representation of words and texts underlies many text processing tasks such as text categorization, word sense disambiguation, and semantic relatedness assessment. It has long been recognized that computers require access to common-sense and domain-specific world knowledge in order to process textual data at a deeper level. In this paper, we present a novel representation of ...

Traditional search engines based on syntactic search are unable to solve key issues like synonymy and polysemy. Solving these issues leads to the invention of the semantic web. The semantic search engines indeed overcome these issues. Nowadays the most important part of the data remains unstructured documents. It is consequently very time consuming to annotate such big data. Concept based retri...

In two experiments, we explored the effects of co-occurrence and semantic relationships in the associative priming of faces. In Experiment 1, pairs of computer-generated human faces were presented simultaneously (i.e., they co-occurred) with no associated semantic information attached to them. A significant facilitation effect in the subsequent recognition of these paired faces (priming) was ob...

Many systems for tasks such as question answering, multi-document summarization, and information retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are based on only a single path between those words in the thesaurus graph. By contrast, we propose a new model of lexical semantic relatedness that incorporates information fro...

N400, an event-related brain potential (ERP) waveform elicited by meaningful stimuli, is normally reduced by stimulus repetition (N400 repetition priming), and relatedness between the eliciting stimulus and preceding ones (relatedness priming). Schizophrenia patients' N400 relatedness priming deficits suggest impairment in using meaningful prime stimuli to facilitate processing of related conce...

A proper semantic representation of words and texts underlies many text processing tasks. In this paper, we present a novel representation of semantics which is based on an hierarchical ontology of natural concepts derived from Wikipedia articles and category system. Our method, called Compact Hierarchical Explicit Semantic Analysis (CHESA) generates compact hierarchical representations of unre...

Mining for semantic information in search engine query logs bears great potential for both the optimization of search engines and bootstrapping Semantic Web applications. The interaction of a user with a search engine (more specifically clicklog information) has recently been viewed as implicit tagging of resources by query terms. The resulting structure – previously called a logsonomy – exhibi...